{"id":3700,"date":"2025-04-25T10:11:41","date_gmt":"2025-04-25T08:11:41","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=3700"},"modified":"2025-10-14T11:05:08","modified_gmt":"2025-10-14T09:05:08","slug":"analyzing-macsima-scan","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/analyzing-macsima-scan\/","title":{"rendered":"Analyzing MACSima 47-plex mIF with MIKAIA: AI Cell Segmentation + Cell Typing + Cell-cell Connections + Cellular Neighborhoods"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">This <a href=\"http:\/\/www.mikaia.ai\">MIKAIA<sup>\u00ae<\/sup><\/a> app note demonstrates how to analyze a 47-plex spatial proteomics scan created with MACSima \u2122 (by Miltenyi Biotec), utilizing sequential immunofluorescence for the analysis:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Instrument<\/strong>: <a href=\"https:\/\/www.miltenyibiotec.com\/DE-en\/products\/macsima-system.html\">MACSima\u2122 by Miltenyi Biotec<\/a><\/li>\n\n\n\n<li><strong>Tissue<\/strong>: Synovial tissue of patient with psoriatic arthritis<\/li>\n\n\n\n<li><strong>Wet lab &amp; Imaging<\/strong>: Fraunhofer ITMP, Frankfurt, Klaus Scholich lab<\/li>\n\n\n\n<li><strong>Panel<\/strong>: 47-plex immune panel composed by Fraunhofer ITMP<\/li>\n\n\n\n<li><strong>Analysis Software<\/strong>: MIKAIA<sup>\u00ae<\/sup> v2.3; apps used: <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-fl-colocalization-app\/\">FL Cell Analysis<\/a>, <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-analysis-of-human-tonsil-15plex-imaged-with-akoya-phenocycler-fusion\/\">Cell-Cell Connections<\/a>, <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-cellular-neighborhood-app\/\">Cellular Neighborhood App<\/a><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/MIKAIA-2.3-MACSima-with-voiceover-1080p30-smaller.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This video was created with MIKAIA<sup>\u00ae<\/sup> 2.3.0 and shows the step-by-step analysis of a 47-plex spatial proteomics scan created with MACSima \u2122. Image generated and kindly provided by colleagues at Fraunhofer ITMP (Prof. Dr. Klaus Scholich, Nathalie Behr). Voice-over \/ speaker: Volker Bruns, Fraunhofer IIS<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Video structure<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Cell segmentation and cell typing<\/strong>\n<ol class=\"wp-block-list\">\n<li>How to import a multiplex image with one TIFF file per marker<\/li>\n\n\n\n<li>Configuring black and white levels per channel using channel-preprocessing preview dialog. <\/li>\n\n\n\n<li>Configuring a cell type mapping (e.g., &#8220;T helper cell&#8221; when CD3 and CD4 are expressed)<\/li>\n\n\n\n<li>Configuring per marker threshold (here the &#8220;auto&#8221; threshold mode is used)<\/li>\n\n\n\n<li>Reviewing and interpreting cell typing results<\/li>\n\n\n\n<li>Interpreting diagrams and working with interactive scatter plot<\/li>\n\n\n\n<li>Alternative unbiased cell typing using clustering, incl. T-SNE &amp; UMAP visualization<\/li>\n\n\n\n<li>Exporting quantitative results to CSV spreadsheet<\/li>\n\n\n\n<li>Enabling interactive heatmap<\/li>\n<\/ol>\n<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-analysis-of-human-tonsil-15plex-imaged-with-akoya-phenocycler-fusion\/\">Cell-cell connections<\/a><\/strong> analysis\n<ul class=\"wp-block-list\">\n<li>The app connects each cell to its closest neighbor cells (Delauney triangulation) and collects statistics such as which cell types are connected, what is average distance, bystander analysis, etc.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-cellular-neighborhood-app\/\">Cellular neighborhood<\/a> <\/strong>(CN) analysis\n<ul class=\"wp-block-list\">\n<li>This app centers on each cell and then collects all cells in the vicinity (either k-nearest or by radius or both).<br>It then can do two analyses:\n<ol class=\"wp-block-list\">\n<li>It reports per cell type the average composition of the neighborhood, grouped by increasing distance (histogram)<\/li>\n\n\n\n<li>It identifies cellular neighborhood (CN) types by k-means clustering and assigns each cell to a CN.<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-1024x576.png\" alt=\"\" class=\"wp-image-3707\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-1024x576.png 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-300x169.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-768x432.png 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-1536x864.png 1536w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-370x208.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-270x152.png 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-570x321.png 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14-740x416.png 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-14.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">screenshot of MIKAIA<sup>\u00ae<\/sup>: result of cell segmentation and cell typing of MACSima 47-plex scan<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Our offer<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sourcing, wet lab, imaging, and bioinformatics as a service (<a href=\"mailto:mikaia@iis.fraunhofer.de\">get in touch<\/a> for a fixed price quotation)<\/li>\n\n\n\n<li>Only need a solution for advanced image analysis? Consider purchasing MIKAIA<sup>\u00ae<\/sup> studio through our partners (<a href=\"mailto:mikaia@iis.fraunhofer.de\">get in touch<\/a> for a quotation).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Contacts for Spatial Proteomics Imaging<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"200\" height=\"200\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-11.png\" alt=\"\" class=\"wp-image-3702\" style=\"width:220px;height:auto\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-11.png 200w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-11-150x150.png 150w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Prof. Dr. Klaus Scholich<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Head of Innovation Area Cross-functional Imaging<br><strong>Fraunhofer Institute for Translational Medicine and Pharmacology ITMP<\/strong><br>Theodor-Stern-Kai 7<br>60596&nbsp;Frankfurt am Main<br><a href=\"mailto: klaus.scholich@itmp.fraunhofer.de\">Send email<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"200\" height=\"200\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-12.png\" alt=\"\" class=\"wp-image-3703\" style=\"width:225px;height:auto\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-12.png 200w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-12-150x150.png 150w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Nathalie Behr<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Doctoral Candidate &amp; Application Specialist<br><strong>Fraunhofer Institute for Translational Medicine and Pharmacology ITMP<\/strong><br>Theodor-Stern-Kai 7<br>60596&nbsp;Frankfurt am Main<br><a href=\"mailto: nathalie.behr@itmp.fraunhofer.de\">Send email<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:30.6%\"><div class=\"wp-block-image\">\n<figure class=\"alignleft size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"491\" height=\"258\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-5.png\" alt=\"\" class=\"wp-image-3567\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-5.png 491w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-5-300x158.png 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-5-370x194.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2025\/04\/image-5-270x142.png 270w\" sizes=\"auto, (max-width: 491px) 100vw, 491px\" \/><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<p class=\"wp-block-paragraph\">This project is conducted in the context of <br>and receives funding by <br><a href=\"https:\/\/www.cimd.fraunhofer.de\/en.html\"><strong>Fraunhofer Cluster of Excellence <\/strong><\/a><br><a href=\"https:\/\/www.cimd.fraunhofer.de\/en.html\"><strong>Immune-Mediated Diseases (CIMD)<\/strong><\/a><\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>This MIKAIA\u00ae app note demonstrates how to analyze a 47-plex spatial proteomics scan created with MACSima \u2122 (by Miltenyi Biotec), utilizing sequential immunofluorescence for the analysis: This video was created with MIKAIA\u00ae 2.3.0 and shows the step-by-step analysis of a 47-plex spatial proteomics scan created with MACSima \u2122. Image generated and kindly provided by colleagues [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":3707,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[35,28,24],"tags":[37,7,29,6,109,5],"coauthors":[56],"class_list":["post-3700","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-life-science","category-mikaia-university","category-spatial-biology","tag-ai","tag-mikaia","tag-mikaia-app-note","tag-partner","tag-use-case","tag-video"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Analyzing MACSima 47-plex mIF with MIKAIA<\/title>\n<meta name=\"description\" content=\"Video tutorial: Analyze Miltenyi MACSima 47-plex mIF using MIKAIA for AI cell segmentation, cell typing, cell-cell connections &amp; cellular neighborhoods.\" \/>\n<meta name=\"robots\" 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